{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1. 对连续型特征，可以用哪个函数可视化其分布？直方图、箱体图 boxplot、提琴形图Violinplot。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding:utf-8 -*-\n",
    "import sys\n",
    "sys.path.append('C:\\\\Users\\\\84531\\\\Anaconda3\\\\Lib\\\\site-packages')\n",
    "#reload(sys)\n",
    "#sys.setdefaultencoding(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296       15   \n",
       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242       17   \n",
       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242       17   \n",
       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222       18   \n",
       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222       18   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:\\\\XYX_use\\\\AI_course\\\\AI_learn\\\\week4\\\\BostonHousePrice_CodeData\\\\boston_housing.csv')\n",
    "#显示前5行\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null int64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null int64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null int64\n",
      "PTRATIO    506 non-null int64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(9), int64(5)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.347826</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.083004</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.310593</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.280574</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677082</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.347826   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.310593    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677082   12.000000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.083004  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.280574   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.000000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.000000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.000000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.000000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数值特征，得到每个特征的统计量\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（房屋价格）的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(df[\"MEDV\"], bins=30, kde=True)  #bins表示直方图的个数，kde表示直方图的密度估计显示，就是图中的纵坐标\n",
    "#http://seaborn.pydata.org/generated/seaborn.distplot.html\n",
    "plt.xlabel(\"Median value of owner-occupied homes\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axes = plt.subplots(1, 2, sharey=True, figsize=(6, 4))\n",
    "sns.boxplot(data=df[\"MEDV\"], ax=axes[0]); \n",
    "sns.violinplot(data=df[\"MEDV\"], ax=axes[1]);"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d05bd3978>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols = df.columns \n",
    "# Calculates pearson co-efficient for all combinations\n",
    "data_corr = df.corr()\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 得到相关系数的绝对值，通常认为相关系数的绝对值大于0.5的特征为强相关\n",
    "data_corr = data_corr.abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features,突出重要信息\n",
    "sns.heatmap(data_corr, mask=data_corr < 0.5, cbar=False)\n",
    "\n",
    "#plt.savefig(\"house_coor.png\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RAD and TAX = 0.91\n",
      "NOX and DIS = 0.77\n",
      "INDUS and NOX = 0.76\n",
      "AGE and DIS = 0.75\n",
      "LSTAT and MEDV = 0.74\n",
      "NOX and AGE = 0.73\n",
      "INDUS and TAX = 0.72\n",
      "INDUS and DIS = 0.71\n",
      "RM and MEDV = 0.70\n",
      "NOX and TAX = 0.67\n",
      "ZN and DIS = 0.66\n",
      "INDUS and AGE = 0.64\n",
      "CRIM and RAD = 0.63\n",
      "RM and LSTAT = 0.61\n",
      "NOX and RAD = 0.61\n",
      "INDUS and LSTAT = 0.60\n",
      "AGE and LSTAT = 0.60\n",
      "INDUS and RAD = 0.60\n",
      "NOX and LSTAT = 0.59\n",
      "CRIM and TAX = 0.58\n",
      "ZN and AGE = 0.57\n",
      "TAX and LSTAT = 0.54\n",
      "DIS and TAX = 0.53\n",
      "ZN and INDUS = 0.53\n",
      "ZN and NOX = 0.52\n",
      "AGE and TAX = 0.51\n",
      "PTRATIO and MEDV = 0.51\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "如果特征之间高度相关，可考虑进行PCA降维（特征层面）或加正则项（模型层面）"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 （10分），并重新训练最小二乘线性回归、岭回归、和Lasso模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从原始数据中分离输入特征X和标签y\n",
    "y=df[\"MEDV\"]\n",
    "X=df.drop(\"MEDV\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RAD的含义是距离高速公路的便利指数。虽然给的数值是数值型，但实际是索引，可换成离散特征/类别型特征编码试试。\n",
    "X[\"RAD\"].astype(\"object\")\n",
    "X_cat = X[\"RAD\"]\n",
    "X_cat = pd.get_dummies(X_cat, prefix=\"RAD\")\n",
    "\n",
    "X = X.drop(\"RAD\", axis = 1)\n",
    "\n",
    "#特征名称，用于保存特征工程结果\n",
    "feat_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "      <th>RAD_1</th>\n",
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       "   RAD_1  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  RAD_8  RAD_24\n",
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cat.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "最小最大缩放（MinMaxScaler）去量纲 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# 对训练数据，先调用fit方法训练模型，得到模型参数；然后对训练数据和测试数据进行transform\n",
    "X = ss_X.fit_transform(X)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y = ss_y.fit_transform(y.values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_data = pd.DataFrame(data = X, columns = feat_names, index = df.index)\n",
    "fe_data = pd.concat([fe_data, X_cat], axis = 1, ignore_index=False)\n",
    "\n",
    "#加上标签y\n",
    "fe_data[\"MEDV\"] = y\n",
    "\n",
    "#保存结果到文件\n",
    "fe_data.to_csv('C:\\\\XYX_use\\\\AI_course\\\\AI_learn\\\\AI_homework\\\\week4\\\\FE_boston_housing.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "\n",
       "        TAX  PTRATIO  ...  RAD_1  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  \\\n",
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       "\n",
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     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "重新训练最小二乘线性回归、岭回归、和Lasso模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.631111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.693333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX  PTRATIO  ...  RAD_1  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  \\\n",
       "0  0.208015      0.3  ...      1      0      0      0      0      0      0   \n",
       "1  0.104962      0.5  ...      0      1      0      0      0      0      0   \n",
       "2  0.104962      0.5  ...      0      1      0      0      0      0      0   \n",
       "3  0.066794      0.6  ...      0      0      1      0      0      0      0   \n",
       "4  0.066794      0.6  ...      0      0      1      0      0      0      0   \n",
       "\n",
       "   RAD_8  RAD_24      MEDV  \n",
       "0      0       0  0.422222  \n",
       "1      0       0  0.368889  \n",
       "2      0       0  0.660000  \n",
       "3      0       0  0.631111  \n",
       "4      0       0  0.693333  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "df = pd.read_csv(\"C:\\\\XYX_use\\\\AI_course\\\\AI_learn\\\\AI_homework\\\\week4\\\\FE_boston_housing.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df[\"MEDV\"]\n",
    "\n",
    "X = df.drop([\"MEDV\"], axis = 1)\n",
    "\n",
    "#特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 21)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "训练最小二乘回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.6939789810509472 \n",
      "\n",
      "The r2 score of LinearRegression on train is 0.7549146436868177 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3. 用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print( 'The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr), \"\\n\")\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr), \"\\n\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6993968315326656\n",
      "The r2 score of RidgeCV on train is 0.7545175686251018\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#1. 设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "n_alphas = 20\n",
    "alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#2. 生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#3. 模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#4. 预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print( 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "找到岭回归最佳参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.26366508987303555\n"
     ]
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Lasso回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.5984974272171608\n",
      "The r2 score of LassoCV on train is 0.5440462858765415\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\84531\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#1. 设置超参数搜索范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#2. 生成学习器实例\n",
    "lasso = LassoCV(alphas=alphas)\n",
    "\n",
    "#1. 设置超参数搜索范围\n",
    "#Lasso可以自动确定最大的alpha，所以另一种设置alpha的方式是设置最小的alpha值（eps） 和 超参数的数目（n_alphas），\n",
    "#然后LassoCV对最小值和最大值之间在log域上均匀取值n_alphas个\n",
    "# np.logspace(np.log10(alpha_max * eps), np.log10(alpha_max),num=n_alphas)[::-1]\n",
    "\n",
    "#2 生成LassoCV实例（默认超参数搜索范围）\n",
    "#lasso = LassoCV()  \n",
    "\n",
    "#3. 训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#4. 测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Lasso回归最佳参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.01\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "5. 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "岭回归和Lasso都是带有惩罚项的最小二乘，如果输入变量存在强相关性或者输入变量过多，防止模型过于复杂，就在最小二乘的基础上引入了参数惩罚项，分别是岭回归和Lasso。如果训练数据过少，需要使用岭回归或Lasso。\n",
    "岭回归得到的参数倾向于使用所有的数据，即每个参数基本不会为0，而Lasso倾向于得到一个稀疏解。"
   ]
  }
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